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Robust Multiple Fault Isolation Based on Partial-orthogonality Criteria

Nicholas Cartocci*, Francesco Crocetti, Gabriele Costante, Paolo Valigi, and Mario L. Fravolini
International Journal of Control, Automation, and Systems, vol. 20, no. 7, pp.2148-2158, 2022

Abstract : "In this paper, a data-driven scheme for the robust Fault Isolation of multiple sensor faults is proposed. Robustness to modelling uncertainty and noise is achieved via the optimized design of the processing blocks. The main idea of the study is the introduction of a Pre-Isolation block that selects a restricted set of sensors containing (with high probability) the subset of the faulty sensors; in this phase, robustness is achieved through the datadriven design of a redundant number of Multiple Analytic Redundancy Relations (MARRs) and a voting logic for the ranking of the candidate faulty sensors. Then, robust Faults Isolation (FI) is achieved by means of another large set of specialized ARRs, whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling only of the pre-isolated fault directions (Partial-Orthogonality Criteria). The proposed diagnostic system may provide an effective means for early sensor failure isolation, particularly useful for critical applications such as aerospace control systems or energy management systems. To assess the performance of the approach, we performed a comparative study with other State-of-the-Art (SoA) approaches using a well-known benchmark model that emulates faults on six sensors. Results for single and multi-contemporary faults have clearly highlighted the superiority of our method. "

Keyword : Data-driven, directional residuals, multiple analytical redundancy, multiple-fault diagnosis, partialorthogonality robust residuals.

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